Applying Causal Machine Learning to Spatiotemporal Data Analysis: An Investigation of Opportunities and Challenges

Traditional spatiotemporal data analysis often relies on predictive models, which overlook the causal relationships driving observed phenomena. This limitation makes it hard to identify true drivers and develop effective interventions. To address this, we explore causal machine learning techniques for spatiotemporal data, aiming to provide robust and interpretable insights. Our review of the literature found that less than 1% of studies, based on rigorous criteria, explicitly combine causal machine learning with spatiotemporal data analysis. Most of these studies focus on improving causal effect discovery and estimation (32 papers), enhancing prediction accuracy (19 papers), and addressing pattern recognition limitations (10 papers). Surprisingly, only one paper discusses using causal machine learning to improve algorithm interpretability for spatiotemporal data, highlighting a major gap. We examine the unique aspects of spatiotemporal data, such as spatial autocorrelation and temporal dependencies, which present both opportunities and challenges for causal inference. Specific methods like spatiotemporal Granger causality and structural equation modeling with spatial lags show potential in capturing complex interdependencies and providing interpretable results. Building on these insights, we propose novel conceptual approaches to model underlying causal structures and suggest promising research directions, such as developing interpretable models and advancing real-time causal inference algorithms in dynamic environments. We also discuss computational challenges like scalability, efficiency, and the trade-offs between model complexity and interpretability that need to be addressed for practical applications. Ethical considerations, including mitigating biases in causal discovery and their societal implications, are also covered. This research contributes to a comprehensive framework for leveraging causal machine learning in spatiotemporal data analysis, with applications in fields like climate science, economics, epidemiology, and urban planning.